{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PassengerId: 乘客id号, Survived: 是否是幸存者, 1: 存活, 2: 死亡\n",
    "# Pclass: 船舱等级, 数字越小等级越高, Name: 乘客姓名\n",
    "# Sex: 乘客性别, male: 男, female: 女,  Age: 乘客年龄, SibSp: 兄弟姐妹人数\n",
    "# Parch: 父母和小孩人数, Ticket: 票编号, Fare: 票价格\n",
    "# Cabin: 船舱号, Embarked: 起始点, 即登船口\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计数值类型分布\n",
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.float64'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(dataset.Age[888]))\n",
    "np.isnan(dataset.Age[888])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看空值个数\n",
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 全部控制填充\n",
    "# dataset.fillna(0)\n",
    "# 指定字段填充\n",
    "# dataset.Age.fillna(23)\n",
    "# 某一字段中位数中位数\n",
    "age_median = dataset.Age.median()\n",
    "age_median\n",
    "# inplace=True 直接在当前dataframe中替换, 默认为False\n",
    "# dataset.Age.fillna(age_median)\n",
    "dataset.Age = dataset.Age.fillna(age_median)\n",
    "# dataset.Age[888]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.0\n",
       "1    38.0\n",
       "2    26.0\n",
       "3    35.0\n",
       "4    35.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Age'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 性别统计\n",
    "dataset['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "female    233\n",
      "male      109\n",
      "Name: Sex, dtype: int64\n",
      "male      468\n",
      "female     81\n",
      "Name: Sex, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 生还者性别统计\n",
    "survived = dataset[dataset.Survived == 1].Sex.value_counts()\n",
    "print(survived)\n",
    "# 未生还者统计\n",
    "dead = dataset[dataset.Survived == 0]['Sex'].value_counts()\n",
    "print(dead)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>233</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dead</th>\n",
       "      <td>81</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          female  male\n",
       "survived     233   109\n",
       "dead          81   468"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([survived, dead], index=['survived', 'dead'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f24293a0b38>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f24272f3dd8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.T.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f24272d9470>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.T.plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "female    314\n",
       "male      577\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['f'] = df.female / (df.female + df.male)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['m'] = df.male / (df.female + df.male)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f24272469b0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 性别和是否生还的比例关系\n",
    "df[['f','m']].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.00    202\n",
       "24.00     30\n",
       "22.00     27\n",
       "18.00     26\n",
       "19.00     25\n",
       "30.00     25\n",
       "21.00     24\n",
       "25.00     23\n",
       "36.00     22\n",
       "29.00     20\n",
       "32.00     18\n",
       "27.00     18\n",
       "35.00     18\n",
       "26.00     18\n",
       "16.00     17\n",
       "31.00     17\n",
       "34.00     15\n",
       "20.00     15\n",
       "23.00     15\n",
       "33.00     15\n",
       "39.00     14\n",
       "17.00     13\n",
       "42.00     13\n",
       "40.00     13\n",
       "45.00     12\n",
       "38.00     11\n",
       "50.00     10\n",
       "2.00      10\n",
       "4.00      10\n",
       "47.00      9\n",
       "        ... \n",
       "71.00      2\n",
       "59.00      2\n",
       "63.00      2\n",
       "0.83       2\n",
       "30.50      2\n",
       "70.00      2\n",
       "57.00      2\n",
       "0.75       2\n",
       "13.00      2\n",
       "10.00      2\n",
       "64.00      2\n",
       "40.50      2\n",
       "32.50      2\n",
       "45.50      2\n",
       "20.50      1\n",
       "24.50      1\n",
       "0.67       1\n",
       "14.50      1\n",
       "0.92       1\n",
       "74.00      1\n",
       "34.50      1\n",
       "80.00      1\n",
       "12.00      1\n",
       "36.50      1\n",
       "53.00      1\n",
       "55.50      1\n",
       "70.50      1\n",
       "66.00      1\n",
       "23.50      1\n",
       "0.42       1\n",
       "Name: Age, Length: 88, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.Age.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "survived1 = dataset[dataset.Survived == 1].Age\n",
    "dead1 = dataset[dataset.Survived == 0].Age\n",
    "df1 = pd.DataFrame([survived1, dead1], index=['survived', 'dead'])\n",
    "df1 = df1.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>dead</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  dead\n",
       "0       NaN  22.0\n",
       "1      38.0   NaN\n",
       "2      26.0   NaN\n",
       "3      35.0   NaN\n",
       "4       NaN  35.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f24271c4da0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot.hist(stacked=True, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2427060cc0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dataset.Age.describe()\n",
    "df1.plot.kde(xlim=(0, 80))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dead</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>minor</th>\n",
       "      <td>69</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>middle</th>\n",
       "      <td>438</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elderly</th>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dead  survived\n",
       "minor      69        70\n",
       "middle    438       250\n",
       "elderly    42        22"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 聚类处理\n",
    "minor = dataset[dataset.Age <= 18].Survived.value_counts()\n",
    "middle = dataset[(dataset.Age > 18) & (dataset.Age <= 50)].Survived.value_counts()\n",
    "elderly = dataset[dataset.Age > 50].Survived.value_counts()\n",
    "df2 = pd.DataFrame([minor, middle, elderly], index=['minor', 'middle', 'elderly'])\n",
    "df2.columns = ['dead', 'survived']\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2417ed4160>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2417caaeb8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived2 = dataset[dataset.Survived == 1].Fare\n",
    "dead2 = dataset[dataset.Survived == 0].Fare\n",
    "df2 = pd.DataFrame([survived2, dead2], index=['survived', 'dead'])\n",
    "df2 = df2.T\n",
    "# df2.plot.kde()\n",
    "df2.plot(kind='kde', xlim=(0, 513))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      32.204208\n",
       "std       49.693429\n",
       "min        0.000000\n",
       "25%        7.910400\n",
       "50%       14.454200\n",
       "75%       31.000000\n",
       "max      512.329200\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.Fare.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'fare')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 组合特征 散点图\n",
    "ages = dataset[dataset.Survived == 0]['Age']\n",
    "fares = dataset[dataset.Survived == 0]['Fare']\n",
    "ages1 = dataset[dataset.Survived == 1]['Age']\n",
    "fares1 = dataset[dataset.Survived == 1]['Fare']\n",
    "draw = plt.subplot()\n",
    "draw.scatter(ages, fares, s=20, alpha=0.6)\n",
    "draw.scatter(ages1, fares1, s=20, alpha=0.5, c='red')\n",
    "draw.set_xlabel('age')\n",
    "draw.set_ylabel('fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Mr\n",
       "1     Mrs\n",
       "2    Miss\n",
       "3     Mrs\n",
       "4      Mr\n",
       "Name: call, dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 称呼\n",
    "dataset['call'] = dataset.Name.apply(lambda name: name.split(',')[1].split('.')[0].strip())\n",
    "dataset['call'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr              517\n",
       "Miss            182\n",
       "Mrs             125\n",
       "Master           40\n",
       "Dr                7\n",
       "Rev               6\n",
       "Major             2\n",
       "Mlle              2\n",
       "Col               2\n",
       "Lady              1\n",
       "the Countess      1\n",
       "Mme               1\n",
       "Don               1\n",
       "Jonkheer          1\n",
       "Capt              1\n",
       "Ms                1\n",
       "Sir               1\n",
       "Name: call, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以通过称呼来推测年龄\n",
    "dataset.call.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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